BackTrack – Standalone YouTube Media Library

Hacker News

Hi HN! I created BackTrack, a standalone web app for your YouTube video library. Why it exists? I made this project because I was looking for an offline YouTube video library solution with background playback. I have looked at TubeArchivist, but it requires me running my own server. In addition, I also looked at apps like NewPipe, but it is only available for Android, while I am using iOS. How it works? For adding videos, it can be either from pasting a YouTube link, or directly search from the web app, the search result is scraped from DuckDuckGo. For retrieving the video metadata or downloading video, it calls YouTube API for a specific android app version, which doesn’t encrypt the video URL. I think they still have this for backward compatibility, as the newer version API will return the video URL with signature/token. And in order to bypass the CORS restriction for both DDG and YT, I am using a CORS proxy. For storage, it uses IndexedDB, which supports storing binaries. For background playback and media controls, it uses plain old audio tag and Media Session API. Hope you enjoy and find it useful.

Visit product View on Hacker NewsSep 19, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created, ios · Missing: reddit linkedin, podcasting, latex

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, new, using · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, video · Missing: mobile apps, personal, entrepreneurs

55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, pipe · Missing: https docs, excited, just released

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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